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Parameter control aims at realizing performance gains through a dynamicchoice of the parameters which determine the behavior of the underlyingoptimization algorithm. In the context of evolutionary algorithms this researchline has for a long time been dominated by empirical approaches. With thesignificant advances in running time analysis achieved in the last ten years,the parameter control question has become accessible to theoreticalinvestigations. A number of running time results for a broad range of differentparameter control mechanisms have been obtained in recent years. This bookchapter surveys these works, and puts them into context, by proposing anupdated classification scheme for parameter control.

Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices